Understanding temporal information in video sequences is crucial for various computer vision tasks, such as action recognition. Transformer-based methods and GCNs can effectively handle temporal information, but they ...
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ISBN:
(数字)9798350368604
ISBN:
(纸本)9798350368611
Understanding temporal information in video sequences is crucial for various computer vision tasks, such as action recognition. Transformer-based methods and GCNs can effectively handle temporal information, but they suffer from high computational costs or poor generalization capabilities. Some research has shown promising results using 3D CNNs to process skeleton data instead of RGB data, but 3D CNNs may treat temporal dimension as a spatial one, limiting their ability to capture complex temporal dynamics. To address these issues, we propose the Multi-Dimensional Attention Fusion module (MDAF), which includes separate channel, spatial, and temporal attention computation modules, integrated through an attention fusion module. This design ensures the effective utilization of attention information across all dimensions. Our method enhances temporal dynamics, improving the recognition of complex actions. Experiments conducted on four benchmark datasets demonstrate that our approach outperforms state-of-the-art methods, achieving significant performance improvements.
The process of educating and learning about the automotive industry is still ongoing, and one of the most effective techniques is the provision of direct practice. Such a system is regarded as effective, albeit with c...
The process of educating and learning about the automotive industry is still ongoing, and one of the most effective techniques is the provision of direct practice. Such a system is regarded as effective, albeit with certain downsides. One of them is the restricted area and time required to conduct the exercise, as well as the significant risk of accidents during vehicle engine assembly. Based on these justifications, Vehicle Engine Assembly Simulation with Virtual Reality (VR) Technology is now available as an alternate means of educating students about the automotive industry, particularly car engine assembly. This concept employs a virtual environment that allows users to perceive the automobile world in real time through an immersive experience. VR allows users to directly disassemble automobile engines. This virtual reality application was created utilizing the Oculus Quest 2 platform and the Unity Game Engine.
Many solutions to address the challenge of robot learning have been devised, namely through exploring novel ways for humans to communicate complex goals and tasks in reinforcement learning (RL) setups. One way that ex...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Many solutions to address the challenge of robot learning have been devised, namely through exploring novel ways for humans to communicate complex goals and tasks in reinforcement learning (RL) setups. One way that experienced recent research interest directly addresses the problem by considering human feedback as preferences between pairs of trajectories (sequences of state-action pairs). However, when simply attributing a single preference to a pair of trajectories that contain many agglomerated steps, key pieces of information are lost in the process. We amplify the initial definition of preferences to account for highlights: state-action pairs of relatively high information (high/low reward) within a preferred trajectory. To include the additional information, we design novel regularization methods within a preference learning framework. To this extent, we present our method which is able to greatly reduce the necessary amount of preferences, by permitting the highlighting of favoured trajectories, in order to reduce the entropy of the credit assignment. We show the effectiveness of our work in both simulation and a user study, which analyzes the feedback given and its implications. We also use the total collected feedback to train a robot policy for socially compliant trajectories in a simulated social navigation environment. We release code and video examples at https://***/view/rl-polite
作者：
Matti VahsJana TumovaDivision of Robotics
Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden
Uncertainties arising in various control systems, such as robots that are subject to unknown disturbances or environmental variations, pose significant challenges for ensuring system safety, such as collision avoidanc...
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ISBN:
(数字)9783907144107
ISBN:
(纸本)9798331540920
Uncertainties arising in various control systems, such as robots that are subject to unknown disturbances or environmental variations, pose significant challenges for ensuring system safety, such as collision avoidance. At the same time, safety specifications are getting more and more complex, e.g., by composing multiple safety objectives through Boolean operators resulting in non-smooth descriptions of safe sets. Control Barrier Functions (CBFs) have emerged as a control technique to provably guarantee system safety. In most settings, they rely on an assumption of having deterministic dynamics and smooth safe sets. This paper relaxes these two assumptions by extending CBFs to encompass control systems with stochastic dynamics and safe sets defined by non-smooth functions. By explicitly considering the stochastic nature of system dynamics and accommodating complex safety specifications, our method enables the design of safe control strategies in uncertain and complex systems. We provide formal guarantees on the safety of the system by leveraging the theoretical foundations of stochastic CBFs and non-smooth safe sets. Numerical simulations demonstrate the effectiveness of the approach in various scenarios.
This study explores robotic telepresence in classrooms, from the perspectives of both the remote students (operators) and their in-person classmates. Analyzing a subset of field study data from 35 participants (22 ope...
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In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and ...
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In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.
In this paper, we propose a system that enables visualization under the situation of scattering media such as fog and smoke by Peplography which is scattering media removal system, on a small GPU machine. Compared to ...
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This study is related to a system that enables elderly people to communicate interactively with young people who use existing message exchange services by simply speaking to an avatar on a tablet PC, without having to...
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Sudden cardiac arrest (SCA) is caused due to malfunctioning in heart rhythm, and the patient needs to be hospitalized as early as possible just after the onset of SCA;otherwise, it can lead to the death of the patient...
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Currently, Digital Holographic Microscopy(DHM) is used for researching disease diagnosis or microbes. It cannot obtain the correct three-dimensional (3D) profile for a little noise because biological cells are microsc...
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